Generalist robotics have arrived, powered by advances in mechatronics and robot AI foundation models. But a key bottleneck remains: robots need vast training data for skills like assembly and inspection, and manual demonstrations aren’t scalable. The NVIDIA Isaac GR00T-Dreams blueprint, built on NVIDIA Cosmos, solves this challenge by generating massive synthetic trajectory data from just a single…
]]>Physical AI models enable robots to autonomously perceive, interpret, reason, and interact with the real world. Accelerated computing and simulations are key to developing the next generation of robotics. Physics plays a crucial role in robotic simulation, providing the foundation for accurate virtual representations of robot behavior and interactions within realistic environments.
]]>The application of robotics is rapidly expanding in diverse environments such as smart manufacturing facilities, commercial kitchens, hospitals, warehouse logistics, and agricultural fields. The industry is shifting towards intelligent automation, which requires enhanced robot capabilities to perform functions including perception, mapping, navigation, load handling, object grasping…
]]>We are announcing our collaboration with Intrinsic.ai on learning foundation skill models for industrial robotics tasks. Many pick-and-place problems in industrial manufacturing are still completed by human operators as it is still challenging to program robots for these tasks. For instance, in a machine-tending setting, a collaborative robot could be used to pick raw material parts from a…
]]>Autonomous machine development is an iterative process of data generation and gathering, model training, and deployment characterized by complex multi-stage, multi-container workflows across heterogeneous compute resources. Multiple teams are involved, each requiring shared and heterogeneous compute. Furthermore, teams want to scale certain workloads into the cloud…
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